Encoding apparatus for processing an input signal and decoding apparatus for processing an encoded signal

Abstract

Disclosed is an apparatus for processing an input signal, having a perceptual weighter and a quantizer. The perceptual weighter has a model provider and a model applicator. The model provider provides a perceptual weighted model based on the input signal. The model applicator provides a perceptually weighted spectrum by applying the perceptual weighted model to a spectrum based on the input signal. The quantizer is configured to quantize the perceptually weighted spectrum and for providing a bitstream. The quantizer has a random matrix applicator and a sign function calculator. The random matrix applicator is configured for applying a random matrix to the perceptually weighted spectrum in order to provide a transformed spectrum. The sign function calculator is configured for calculating a sign function of components of the transformed spectrum in order to provide the bitstream. The invention further refers to an apparatus for processing an encoded signal and to corresponding methods.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority from European Application No. 16160369.1, which was filed on Mar. 15, 2016, and from European Application No. 16189893.7, which was filed on Sep. 21, 2016, which are both incorporated herein by reference in their entirety.BACKGROUND OF THE INVENTION[0002]The invention refers to an encoding apparatus for processing an input signal and to a decoding apparatus for processing an encoded signal. The invention also refers to corresponding methods and to a computer program.[0003]A central part of speech and audio codecs are their perceptual models, which describe the relative perceptual importance of errors in different elements of the signal representation. In practice, the perceptual models consist of signal-dependent weighting factors which are used in quantization of each element. For optimal performance, it would be desirable to use the same perceptual model at the decoder. While the perceptual model is sign...

AI Technical Summary

Benefits of technology

[0065]The perceptual de-weighter reverses the effects of a perceptual weighting that happened during the encoding process leading to the encoded signal. This is done in one embodiment without side-information of the encoded signal comprising the perceptual weighted model. The model is reconstructed from the encoded audio signal as such.

Problems solved by technology

While the perceptual model is signal-dependent, however, it is not known in advance at the decoder, whereby audio codecs generally transmit this model explicitly, at the cost of increased bit-consumption.

The design goals of IoT-systems however fit poorly with the classic design of speech and audio coders, whereby a larger redesign of the coders is necessitated.

Secondly, since sensor-nodes are encoding the same source signal, application of the same quantization at each sensor-node would represent over-coding and potentially a serious loss in efficiency.

Though here the perceptual model has not to be transmitted, this approach has the disadvantage that quantization cells shapes are not regularly shaped which reduces coding efficiency.

More importantly, however, to find the optimal quantization, a computationally complex brute-force search of different quantizations has to be used.

Since the analysis-by-synthesis approach thus leads to a computationally complex encoder, it is not a viable alternative for IoT.

However, as noted above, explicit transmission of the perceptual model (or equivalently, an envelope model of the signal spectrum), is not desirable because it lowers coding efficiency.

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